Modeling and Topology Estimation of Low Rank Dynamical Networks
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912697845547008 |
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| author | Cao, Wenqi Li, Aming |
| author_facet | Cao, Wenqi Li, Aming |
| contents | Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling and Topology Estimation of Low Rank Dynamical Networks Cao, Wenqi Li, Aming Graphics Machine Learning Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach. |
| title | Modeling and Topology Estimation of Low Rank Dynamical Networks |
| topic | Graphics Machine Learning |
| url | https://arxiv.org/abs/2511.06674 |